Cloud API Vendor Data Filtering for KPI Accuracy
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Solution Overview
Problem
Telecommunication network providers face difficulties in accurately measuring key-performance-indicators (KPIs) due to vendor testing data being mixed with consumer data, leading to over-indexed measurement results and an inaccurate view of device and software performance.
Innovation Solution
Integration of network provider performance analytics with vendor testing automation using a cloud-based API to filter out vendor testing data, allowing for accurate measurement of consumer device performance by identifying and excluding vendor testing UEs and technologies through a database system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If vendor testing data is collected along with consumer data, then the volume of data available for analysis increases, but the measurement precision of KPIs deteriorates due to mixed data sources
Solution Approach 1:
The patent segments vendor testing data from consumer data by creating separate identification and filtering mechanisms. Vendor UEs are identified through unique identifiers or testing pattern recognition, allowing the system to separate vendor data streams from consumer data streams for independent analysis.
Solution Approach 2:
The patent extracts vendor testing data from the mixed data pool through identification rules and filtering algorithms. Once identified, vendor data is removed or excluded from KPI calculations, leaving only consumer data for accurate performance measurement while preserving the option to analyze vendor data separately.
2Quantity of substance
If vendor UEs are included in performance measurements, then the quantity of measured devices increases, but the reliability of performance results worsens due to non-representative testing data
Solution Approach 1:
The patent applies preliminary identification and classification of UEs as vendor or consumer devices before performance measurement. By pre-tagging or pre-filtering vendor UEs based on identifier matching or testing behavior patterns, the system prevents contaminating the consumer performance dataset while maintaining comprehensive device coverage.
Solution Approach 2:
The patent introduces an intermediary filtering layer between data collection and KPI calculation. This intermediary component identifies vendor UEs and either excludes them from consumer KPI calculations or routes them to separate analysis pipelines, ensuring that vendor testing data does not directly contaminate consumer performance metrics.
3Productivity
If comprehensive device testing is performed, then the productivity of device evaluation increases, but the loss of information worsens when vendor and consumer data are mixed
Solution Approach 1:
The patent applies metaphorical 'color coding' through data tagging and labeling mechanisms. Vendor UEs and consumer UEs are marked with distinct identifiers or metadata tags that preserve source information throughout the data pipeline, allowing comprehensive collection while maintaining clear distinction between data sources for accurate analysis.
Data Source
AI summary
A processor-implemented method includes integrating telecommunication network provider performance analytics with vendor testing automation such that specific data from vendor user equipment (UE) testing can be filtered out and the performance data results represent true service performance for the desired UEs. A network device associated with the network provider may establish an application programming interface (API) with a vendor device associated with the vendor to receive vendor testing information used to identify UEs undergoing vendor testing. The network device may identify the vendor UEs undergoing vendor testing when performing measurements on a group of UEs, such as key performance indicator (KPI) measurements.